Hybrid Recommendation System for the Marketing of Artisanal Agricultural Products in Dispersed Rural Markets

Artisanal farmers in developing regions capture limited commercial value due to information asymmetries, market fragmentation, and informal intermediation, where commercialization, rather than production, is the main bottleneck for rural income. This study formalizes artisanal agricultural commercialization as a buyer-recommendation problem in a sparse bilateral market. We propose a context-aware hybrid system that combines content-based and collaborative filtering with economic and geographic variables. Because no transactional dataset exists for the target region (Cesar, Colombia), a proof-of-concept evaluation (Phase A) uses the Brazilian Olist e-commerce dataset as a structural proxy, benchmarking 13 models under a temporal leave-one-out protocol. A Reciprocal Rank Fusion ensemble achieved the best ranking performance (NDCG@10 = 0.4031), an item-based neighborhood model had the lowest rating-prediction error, and content-based filtering proved to be the most informative component under extreme sparsity. Differences were confirmed using Friedman, Nemenyi, bootstrap, and effect size tests. These offline indicators show technical feasibility but do not demonstrate gains in producer income or field adoption rates. Real-world validation, contextual adaptation, and fairness assessment in rural Cesar are proposed for future work (Phase B).